Overview

Dataset statistics

Number of variables15
Number of observations187
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.0 KiB
Average record size in memory120.7 B

Variable types

Categorical2
Numeric13

Alerts

Country/Region has a high cardinality: 187 distinct values High cardinality
Confirmed is highly correlated with Deaths and 7 other fieldsHigh correlation
Deaths is highly correlated with Confirmed and 8 other fieldsHigh correlation
Recovered is highly correlated with Confirmed and 7 other fieldsHigh correlation
Active is highly correlated with Confirmed and 7 other fieldsHigh correlation
New cases is highly correlated with Confirmed and 7 other fieldsHigh correlation
New deaths is highly correlated with Confirmed and 7 other fieldsHigh correlation
New recovered is highly correlated with Confirmed and 7 other fieldsHigh correlation
Deaths / 100 Cases is highly correlated with DeathsHigh correlation
Recovered / 100 Cases is highly correlated with 1 week % increaseHigh correlation
Deaths / 100 Recovered is highly correlated with Deaths / 100 CasesHigh correlation
Confirmed last week is highly correlated with Confirmed and 7 other fieldsHigh correlation
1 week change is highly correlated with Confirmed and 7 other fieldsHigh correlation
1 week % increase is highly correlated with Recovered / 100 CasesHigh correlation
Deaths / 100 Recovered has 5 (2.7%) infinite values Infinite
Country/Region is uniformly distributed Uniform
Country/Region has unique values Unique
Deaths has 17 (9.1%) zeros Zeros
Recovered has 6 (3.2%) zeros Zeros
Active has 5 (2.7%) zeros Zeros
New cases has 33 (17.6%) zeros Zeros
New deaths has 91 (48.7%) zeros Zeros
New recovered has 61 (32.6%) zeros Zeros
Deaths / 100 Cases has 17 (9.1%) zeros Zeros
Recovered / 100 Cases has 6 (3.2%) zeros Zeros
Deaths / 100 Recovered has 17 (9.1%) zeros Zeros
1 week change has 12 (6.4%) zeros Zeros
1 week % increase has 12 (6.4%) zeros Zeros

Reproduction

Analysis started2022-09-23 09:17:32.443901
Analysis finished2022-09-23 09:18:08.867054
Duration36.42 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Country/Region
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct187
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Afghanistan
 
1
Pakistan
 
1
Nepal
 
1
Netherlands
 
1
New Zealand
 
1
Other values (182)
182 

Length

Max length32
Median length22
Mean length8.43315508
Min length2

Characters and Unicode

Total characters1577
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique187 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola

Common Values

ValueCountFrequency (%)
Afghanistan1
 
0.5%
Pakistan1
 
0.5%
Nepal1
 
0.5%
Netherlands1
 
0.5%
New Zealand1
 
0.5%
Nicaragua1
 
0.5%
Niger1
 
0.5%
Nigeria1
 
0.5%
North Macedonia1
 
0.5%
Norway1
 
0.5%
Other values (177)177
94.7%

Length

2022-09-23T14:48:09.044287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and7
 
3.0%
saint3
 
1.3%
south3
 
1.3%
guinea3
 
1.3%
sudan2
 
0.9%
republic2
 
0.9%
united2
 
0.9%
congo2
 
0.9%
new2
 
0.9%
brazil1
 
0.4%
Other values (207)207
88.5%

Most occurring characters

ValueCountFrequency (%)
a247
15.7%
i140
 
8.9%
n128
 
8.1%
e111
 
7.0%
r88
 
5.6%
o84
 
5.3%
t58
 
3.7%
u56
 
3.6%
l51
 
3.2%
d49
 
3.1%
Other values (47)565
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1293
82.0%
Uppercase Letter229
 
14.5%
Space Separator47
 
3.0%
Open Punctuation2
 
0.1%
Close Punctuation2
 
0.1%
Dash Punctuation2
 
0.1%
Other Punctuation2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a247
19.1%
i140
10.8%
n128
9.9%
e111
 
8.6%
r88
 
6.8%
o84
 
6.5%
t58
 
4.5%
u56
 
4.3%
l51
 
3.9%
d49
 
3.8%
Other values (16)281
21.7%
Uppercase Letter
ValueCountFrequency (%)
S29
12.7%
B22
 
9.6%
C18
 
7.9%
M17
 
7.4%
G16
 
7.0%
A15
 
6.6%
L13
 
5.7%
N11
 
4.8%
T11
 
4.8%
E9
 
3.9%
Other values (15)68
29.7%
Other Punctuation
ValueCountFrequency (%)
*1
50.0%
'1
50.0%
Space Separator
ValueCountFrequency (%)
47
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1522
96.5%
Common55
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a247
16.2%
i140
 
9.2%
n128
 
8.4%
e111
 
7.3%
r88
 
5.8%
o84
 
5.5%
t58
 
3.8%
u56
 
3.7%
l51
 
3.4%
d49
 
3.2%
Other values (41)510
33.5%
Common
ValueCountFrequency (%)
47
85.5%
(2
 
3.6%
)2
 
3.6%
-2
 
3.6%
*1
 
1.8%
'1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a247
15.7%
i140
 
8.9%
n128
 
8.1%
e111
 
7.0%
r88
 
5.6%
o84
 
5.3%
t58
 
3.7%
u56
 
3.6%
l51
 
3.2%
d49
 
3.1%
Other values (47)565
35.8%

Confirmed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct184
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88130.93583
Minimum10
Maximum4290259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-09-23T14:48:09.246694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile33.3
Q11114
median5059
Q340460.5
95-th percentile287810.9
Maximum4290259
Range4290249
Interquartile range (IQR)39346.5

Descriptive statistics

Standard deviation383318.6638
Coefficient of variation (CV)4.349422371
Kurtosis86.09657177
Mean88130.93583
Median Absolute Deviation (MAD)4973
Skewness8.725676203
Sum16480485
Variance1.46933198 × 1011
MonotonicityNot monotonic
2022-09-23T14:48:09.453774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242
 
1.1%
862
 
1.1%
106212
 
1.1%
1095971
 
0.5%
91321
 
0.5%
18431
 
0.5%
187521
 
0.5%
534131
 
0.5%
15571
 
0.5%
34391
 
0.5%
Other values (174)174
93.0%
ValueCountFrequency (%)
101
0.5%
121
0.5%
141
0.5%
171
0.5%
181
0.5%
201
0.5%
231
0.5%
242
1.1%
271
0.5%
481
0.5%
ValueCountFrequency (%)
42902591
0.5%
24423751
0.5%
14800731
0.5%
8166801
0.5%
4525291
0.5%
3954891
0.5%
3897171
0.5%
3479231
0.5%
3017081
0.5%
2936061
0.5%

Deaths
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct150
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3497.518717
Minimum0
Maximum148011
Zeros17
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-09-23T14:48:09.652823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.5
median108
Q3734
95-th percentile15138.6
Maximum148011
Range148011
Interquartile range (IQR)715.5

Descriptive statistics

Standard deviation14100.00248
Coefficient of variation (CV)4.031430172
Kurtosis66.48049433
Mean3497.518717
Median Absolute Deviation (MAD)106
Skewness7.464481094
Sum654036
Variance198810070
MonotonicityNot monotonic
2022-09-23T14:48:09.878829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017
 
9.1%
114
 
2.1%
13
 
1.6%
103
 
1.6%
23
 
1.6%
83
 
1.6%
73
 
1.6%
452
 
1.1%
352
 
1.1%
222
 
1.1%
Other values (140)145
77.5%
ValueCountFrequency (%)
017
9.1%
13
 
1.6%
23
 
1.6%
32
 
1.1%
41
 
0.5%
51
 
0.5%
61
 
0.5%
73
 
1.6%
83
 
1.6%
91
 
0.5%
ValueCountFrequency (%)
1480111
0.5%
876181
0.5%
458441
0.5%
440221
0.5%
351121
0.5%
334081
0.5%
302121
0.5%
284321
0.5%
184181
0.5%
159121
0.5%

Recovered
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct178
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50631.48128
Minimum0
Maximum1846641
Zeros6
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-09-23T14:48:10.090529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.6
Q1626.5
median2815
Q322606
95-th percentile235599
Maximum1846641
Range1846641
Interquartile range (IQR)21979.5

Descriptive statistics

Standard deviation190188.1896
Coefficient of variation (CV)3.756322841
Kurtosis55.60077116
Mean50631.48128
Median Absolute Deviation (MAD)2789
Skewness6.983643759
Sum9468087
Variance3.617154748 × 1010
MonotonicityNot monotonic
2022-09-23T14:48:10.439683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06
 
3.2%
182
 
1.1%
8032
 
1.1%
1282
 
1.1%
392
 
1.1%
251981
 
0.5%
87521
 
0.5%
137541
 
0.5%
1891
 
0.5%
15141
 
0.5%
Other values (168)168
89.8%
ValueCountFrequency (%)
06
3.2%
81
 
0.5%
111
 
0.5%
121
 
0.5%
131
 
0.5%
151
 
0.5%
182
 
1.1%
191
 
0.5%
221
 
0.5%
231
 
0.5%
ValueCountFrequency (%)
18466411
0.5%
13258041
0.5%
9511661
0.5%
6022491
0.5%
3199541
0.5%
3038101
0.5%
2749251
0.5%
2725471
0.5%
2551441
0.5%
2410261
0.5%

Active
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct173
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34001.93583
Minimum0
Maximum2816444
Zeros5
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-09-23T14:48:10.651840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q1141.5
median1600
Q39149
95-th percentile98399.5
Maximum2816444
Range2816444
Interquartile range (IQR)9007.5

Descriptive statistics

Standard deviation213326.1734
Coefficient of variation (CV)6.273942003
Kurtosis157.9216648
Mean34001.93583
Median Absolute Deviation (MAD)1585
Skewness12.18206736
Sum6358362
Variance4.550805625 × 1010
MonotonicityNot monotonic
2022-09-23T14:48:10.854921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
2.7%
13
 
1.6%
23
 
1.6%
15992
 
1.1%
212
 
1.1%
132
 
1.1%
92
 
1.1%
242
 
1.1%
522
 
1.1%
19201
 
0.5%
Other values (163)163
87.2%
ValueCountFrequency (%)
05
2.7%
13
1.6%
23
1.6%
41
 
0.5%
81
 
0.5%
92
 
1.1%
121
 
0.5%
132
 
1.1%
151
 
0.5%
181
 
0.5%
ValueCountFrequency (%)
28164441
0.5%
5081161
0.5%
4954991
0.5%
2544271
0.5%
2010971
0.5%
1705371
0.5%
1171631
0.5%
1089281
0.5%
1075141
0.5%
987521
0.5%

New cases
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct122
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1222.957219
Minimum0
Maximum56336
Zeros33
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-09-23T14:48:11.073169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median49
Q3419.5
95-th percentile2706.3
Maximum56336
Range56336
Interquartile range (IQR)415.5

Descriptive statistics

Standard deviation5710.37479
Coefficient of variation (CV)4.669316882
Kurtosis65.0223296
Mean1222.957219
Median Absolute Deviation (MAD)49
Skewness7.720319623
Sum228693
Variance32608380.25
MonotonicityNot monotonic
2022-09-23T14:48:11.276284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033
 
17.6%
16
 
3.2%
114
 
2.1%
243
 
1.6%
103
 
1.6%
43
 
1.6%
33
 
1.6%
23
 
1.6%
133
 
1.6%
53
 
1.6%
Other values (112)123
65.8%
ValueCountFrequency (%)
033
17.6%
16
 
3.2%
23
 
1.6%
33
 
1.6%
43
 
1.6%
53
 
1.6%
63
 
1.6%
73
 
1.6%
81
 
0.5%
91
 
0.5%
ValueCountFrequency (%)
563361
0.5%
444571
0.5%
232841
0.5%
163061
0.5%
137561
0.5%
70961
0.5%
56071
0.5%
49731
0.5%
48901
0.5%
27721
0.5%

New deaths
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct38
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.95721925
Minimum0
Maximum1076
Zeros91
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-09-23T14:48:11.474216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile92.7
Maximum1076
Range1076
Interquartile range (IQR)6

Descriptive statistics

Standard deviation120.037173
Coefficient of variation (CV)4.145328041
Kurtosis40.10154884
Mean28.95721925
Median Absolute Deviation (MAD)1
Skewness5.970033351
Sum5415
Variance14408.92289
MonotonicityNot monotonic
2022-09-23T14:48:11.689072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
091
48.7%
117
 
9.1%
211
 
5.9%
37
 
3.7%
67
 
3.7%
47
 
3.7%
56
 
3.2%
173
 
1.6%
113
 
1.6%
202
 
1.1%
Other values (28)33
 
17.6%
ValueCountFrequency (%)
091
48.7%
117
 
9.1%
211
 
5.9%
37
 
3.7%
47
 
3.7%
56
 
3.2%
67
 
3.7%
72
 
1.1%
82
 
1.1%
92
 
1.1%
ValueCountFrequency (%)
10761
0.5%
6371
0.5%
6141
0.5%
5751
0.5%
5081
0.5%
3421
0.5%
2981
0.5%
2121
0.5%
1201
0.5%
961
0.5%

New recovered
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct103
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean933.8128342
Minimum0
Maximum33728
Zeros61
Zeros (%)32.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-09-23T14:48:11.876533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22
Q3221
95-th percentile2446.2
Maximum33728
Range33728
Interquartile range (IQR)221

Descriptive statistics

Standard deviation4197.719635
Coefficient of variation (CV)4.495247314
Kurtosis47.91008231
Mean933.8128342
Median Absolute Deviation (MAD)22
Skewness6.769567359
Sum174623
Variance17620850.13
MonotonicityNot monotonic
2022-09-23T14:48:12.104798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
061
32.6%
26
 
3.2%
45
 
2.7%
14
 
2.1%
63
 
1.6%
242
 
1.1%
392
 
1.1%
702
 
1.1%
152
 
1.1%
1032
 
1.1%
Other values (93)98
52.4%
ValueCountFrequency (%)
061
32.6%
14
 
2.1%
26
 
3.2%
32
 
1.1%
45
 
2.7%
52
 
1.1%
63
 
1.6%
71
 
0.5%
81
 
0.5%
112
 
1.1%
ValueCountFrequency (%)
337281
0.5%
335981
0.5%
279411
0.5%
114941
0.5%
98481
0.5%
85881
0.5%
46971
0.5%
35921
0.5%
30771
0.5%
26131
0.5%

Deaths / 100 Cases
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct145
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.019518717
Minimum0
Maximum28.56
Zeros17
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-09-23T14:48:12.307873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.945
median2.15
Q33.875
95-th percentile9.439
Maximum28.56
Range28.56
Interquartile range (IQR)2.93

Descriptive statistics

Standard deviation3.454302488
Coefficient of variation (CV)1.143991084
Kurtosis17.5411832
Mean3.019518717
Median Absolute Deviation (MAD)1.35
Skewness3.352172714
Sum564.65
Variance11.93220568
MonotonicityNot monotonic
2022-09-23T14:48:12.519024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017
 
9.1%
1.283
 
1.6%
2.913
 
1.6%
3.452
 
1.1%
0.952
 
1.1%
0.262
 
1.1%
1.332
 
1.1%
4.822
 
1.1%
1.412
 
1.1%
1.692
 
1.1%
Other values (135)150
80.2%
ValueCountFrequency (%)
017
9.1%
0.051
 
0.5%
0.151
 
0.5%
0.181
 
0.5%
0.262
 
1.1%
0.272
 
1.1%
0.361
 
0.5%
0.391
 
0.5%
0.431
 
0.5%
0.451
 
0.5%
ValueCountFrequency (%)
28.561
0.5%
15.191
0.5%
14.791
0.5%
14.261
0.5%
13.711
0.5%
13.41
0.5%
11.531
0.5%
11.131
0.5%
10.441
0.5%
101
0.5%

Recovered / 100 Cases
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct177
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.82053476
Minimum0
Maximum100
Zeros6
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-09-23T14:48:12.713176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.775
Q148.77
median71.32
Q386.885
95-th percentile96.309
Maximum100
Range100
Interquartile range (IQR)38.115

Descriptive statistics

Standard deviation26.28769426
Coefficient of variation (CV)0.405545779
Kurtosis-0.1157282177
Mean64.82053476
Median Absolute Deviation (MAD)17.31
Skewness-0.8233658854
Sum12121.44
Variance691.0428696
MonotonicityNot monotonic
2022-09-23T14:48:12.916252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06
 
3.2%
1003
 
1.6%
75.612
 
1.1%
80.642
 
1.1%
76.822
 
1.1%
74.011
 
0.5%
97.241
 
0.5%
72.461
 
0.5%
90.721
 
0.5%
44.21
 
0.5%
Other values (167)167
89.3%
ValueCountFrequency (%)
06
3.2%
0.351
 
0.5%
0.481
 
0.5%
5.481
 
0.5%
8.531
 
0.5%
12.681
 
0.5%
17.741
 
0.5%
20.041
 
0.5%
20.251
 
0.5%
20.411
 
0.5%
ValueCountFrequency (%)
1003
1.6%
98.381
 
0.5%
98.331
 
0.5%
97.871
 
0.5%
97.241
 
0.5%
97.021
 
0.5%
96.61
 
0.5%
96.511
 
0.5%
95.841
 
0.5%
95.241
 
0.5%

Deaths / 100 Recovered
Real number (ℝ≥0)

HIGH CORRELATION
INFINITE
ZEROS

Distinct155
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite5
Infinite (%)2.7%
Meaninf
Minimum0
Maximuminf
Zeros17
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-09-23T14:48:13.275871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.45
median3.62
Q36.44
95-th percentile32.982
Maximuminf
Rangeinf
Interquartile range (IQR)4.99

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Meaninf
Median Absolute Deviation (MAD)2.45
Skewnessnan
Suminf
Variancenan
MonotonicityNot monotonic
2022-09-23T14:48:13.478944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017
 
9.1%
inf5
 
2.7%
4.862
 
1.1%
3.912
 
1.1%
12.122
 
1.1%
6.692
 
1.1%
1.452
 
1.1%
3.012
 
1.1%
1.912
 
1.1%
5.252
 
1.1%
Other values (145)149
79.7%
ValueCountFrequency (%)
017
9.1%
0.061
 
0.5%
0.161
 
0.5%
0.21
 
0.5%
0.331
 
0.5%
0.351
 
0.5%
0.391
 
0.5%
0.511
 
0.5%
0.521
 
0.5%
0.551
 
0.5%
ValueCountFrequency (%)
inf5
2.7%
3259.261
 
0.5%
3190.261
 
0.5%
57.981
 
0.5%
56.281
 
0.5%
37.21
 
0.5%
23.141
 
0.5%
18.911
 
0.5%
17.91
 
0.5%
17.681
 
0.5%

Confirmed last week
Real number (ℝ≥0)

HIGH CORRELATION

Distinct183
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78682.47594
Minimum10
Maximum3834677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-09-23T14:48:13.699455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile24.9
Q11051.5
median5020
Q337080.5
95-th percentile273170.2
Maximum3834677
Range3834667
Interquartile range (IQR)36029

Descriptive statistics

Standard deviation338273.6766
Coefficient of variation (CV)4.299225114
Kurtosis89.37688432
Mean78682.47594
Median Absolute Deviation (MAD)4912
Skewness8.865198244
Sum14713623
Variance1.144290803 × 1011
MonotonicityNot monotonic
2022-09-23T14:48:13.918157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192
 
1.1%
5222
 
1.1%
19802
 
1.1%
232
 
1.1%
92491
 
0.5%
15071
 
0.5%
13441
 
0.5%
178441
 
0.5%
521321
 
0.5%
15551
 
0.5%
Other values (173)173
92.5%
ValueCountFrequency (%)
101
0.5%
121
0.5%
131
0.5%
171
0.5%
181
0.5%
192
1.1%
232
1.1%
241
0.5%
271
0.5%
401
0.5%
ValueCountFrequency (%)
38346771
0.5%
21186461
0.5%
11553381
0.5%
7762121
0.5%
3736281
0.5%
3576811
0.5%
3493961
0.5%
3330291
0.5%
2969441
0.5%
2762021
0.5%

1 week change
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct162
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9448.459893
Minimum-47
Maximum455582
Zeros12
Zeros (%)6.4%
Negative1
Negative (%)0.5%
Memory size1.6 KiB
2022-09-23T14:48:14.138713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-47
5-th percentile0
Q149
median432
Q33172
95-th percentile18508
Maximum455582
Range455629
Interquartile range (IQR)3123

Descriptive statistics

Standard deviation47491.12768
Coefficient of variation (CV)5.02633532
Kurtosis61.66273793
Mean9448.459893
Median Absolute Deviation (MAD)431
Skewness7.692012049
Sum1766862
Variance2255407209
MonotonicityNot monotonic
2022-09-23T14:48:14.341795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012
 
6.4%
14
 
2.1%
23
 
1.6%
723
 
1.6%
472
 
1.1%
2012
 
1.1%
272
 
1.1%
982
 
1.1%
92
 
1.1%
112
 
1.1%
Other values (152)153
81.8%
ValueCountFrequency (%)
-471
 
0.5%
012
6.4%
14
 
2.1%
23
 
1.6%
41
 
0.5%
51
 
0.5%
61
 
0.5%
71
 
0.5%
81
 
0.5%
92
 
1.1%
ValueCountFrequency (%)
4555821
0.5%
3247351
0.5%
3237291
0.5%
789011
0.5%
530961
0.5%
460931
0.5%
404681
0.5%
366421
0.5%
320361
0.5%
187721
0.5%

1 week % increase
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct169
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.60620321
Minimum-3.84
Maximum226.32
Zeros12
Zeros (%)6.4%
Negative1
Negative (%)0.5%
Memory size1.6 KiB
2022-09-23T14:48:14.551525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.84
5-th percentile0
Q12.775
median6.89
Q316.855
95-th percentile37.277
Maximum226.32
Range230.16
Interquartile range (IQR)14.08

Descriptive statistics

Standard deviation24.50983774
Coefficient of variation (CV)1.801372313
Kurtosis45.80886493
Mean13.60620321
Median Absolute Deviation (MAD)5.35
Skewness6.11461286
Sum2544.36
Variance600.7321463
MonotonicityNot monotonic
2022-09-23T14:48:14.749233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012
 
6.4%
5.362
 
1.1%
6.892
 
1.1%
12.242
 
1.1%
2.512
 
1.1%
10.422
 
1.1%
3.132
 
1.1%
2.442
 
1.1%
9.281
 
0.5%
12.871
 
0.5%
Other values (159)159
85.0%
ValueCountFrequency (%)
-3.841
 
0.5%
012
6.4%
0.131
 
0.5%
0.261
 
0.5%
0.291
 
0.5%
0.491
 
0.5%
0.641
 
0.5%
0.681
 
0.5%
0.71
 
0.5%
0.781
 
0.5%
ValueCountFrequency (%)
226.321
0.5%
191.071
0.5%
119.541
0.5%
57.851
0.5%
42.781
0.5%
42.521
0.5%
41.571
0.5%
40.671
0.5%
37.441
0.5%
37.341
0.5%

WHO Region
Categorical

Distinct6
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Europe
56 
Africa
48 
Americas
35 
Eastern Mediterranean
22 
Western Pacific
16 

Length

Max length21
Median length6
Mean length9.390374332
Min length6

Characters and Unicode

Total characters1756
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEastern Mediterranean
2nd rowEurope
3rd rowAfrica
4th rowEurope
5th rowAfrica

Common Values

ValueCountFrequency (%)
Europe56
29.9%
Africa48
25.7%
Americas35
18.7%
Eastern Mediterranean22
 
11.8%
Western Pacific16
 
8.6%
South-East Asia10
 
5.3%

Length

2022-09-23T14:48:14.936692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-23T14:48:15.172033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
europe56
23.8%
africa48
20.4%
americas35
14.9%
eastern22
 
9.4%
mediterranean22
 
9.4%
western16
 
6.8%
pacific16
 
6.8%
south-east10
 
4.3%
asia10
 
4.3%

Most occurring characters

ValueCountFrequency (%)
r221
12.6%
e211
12.0%
a185
10.5%
i147
 
8.4%
c115
 
6.5%
A93
 
5.3%
s93
 
5.3%
E88
 
5.0%
n82
 
4.7%
t80
 
4.6%
Other values (13)441
25.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1453
82.7%
Uppercase Letter245
 
14.0%
Space Separator48
 
2.7%
Dash Punctuation10
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r221
15.2%
e211
14.5%
a185
12.7%
i147
10.1%
c115
7.9%
s93
6.4%
n82
 
5.6%
t80
 
5.5%
o66
 
4.5%
u66
 
4.5%
Other values (5)187
12.9%
Uppercase Letter
ValueCountFrequency (%)
A93
38.0%
E88
35.9%
M22
 
9.0%
W16
 
6.5%
P16
 
6.5%
S10
 
4.1%
Space Separator
ValueCountFrequency (%)
48
100.0%
Dash Punctuation
ValueCountFrequency (%)
-10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1698
96.7%
Common58
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r221
13.0%
e211
12.4%
a185
10.9%
i147
 
8.7%
c115
 
6.8%
A93
 
5.5%
s93
 
5.5%
E88
 
5.2%
n82
 
4.8%
t80
 
4.7%
Other values (11)383
22.6%
Common
ValueCountFrequency (%)
48
82.8%
-10
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r221
12.6%
e211
12.0%
a185
10.5%
i147
 
8.4%
c115
 
6.5%
A93
 
5.3%
s93
 
5.3%
E88
 
5.0%
n82
 
4.7%
t80
 
4.6%
Other values (13)441
25.1%

Interactions

2022-09-23T14:48:06.017884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:37.413888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:39.701339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:42.148039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:44.548370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:46.944920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:49.325420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:51.741754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:54.228972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:56.486397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:58.917920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:01.250217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:03.746745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:06.195741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:37.620389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:39.884629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:42.330253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:44.713465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:47.112139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:49.491867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:51.907590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:54.404503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:56.654135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:59.085089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:01.433221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:03.913394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:06.360653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:37.818475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:40.068773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:42.498340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:44.896966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:47.342954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:49.676348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:52.089396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:54.572227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:56.836322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:59.277240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:01.615763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:04.096380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:06.542003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:37.984686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:40.250362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:42.697541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:45.079811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:47.524204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:49.999413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:52.272928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:54.754101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:57.028815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:59.450298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:01.797836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:04.262422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:06.852198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:38.168817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:40.434113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:42.866044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:45.245561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:47.693758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:50.175442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:52.456124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:54.938396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:57.202885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:59.633468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:01.981726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:04.445086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:07.042158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:38.332988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:40.601032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:43.131341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:45.411978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:47.860799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:50.341519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:52.639216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:55.103934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:57.369183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:59.813929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:02.163568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:04.612355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:07.216967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:38.500900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:40.782990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:43.297127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:45.738369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:48.074590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:50.524035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:52.822375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:55.270679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:57.551838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:59.982766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:02.347836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:04.795627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:07.376242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:38.686092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:40.949455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:43.480576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:45.912212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:48.243661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:50.692463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:53.013480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:55.453849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:57.718040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:00.184282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:02.675608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:04.961627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:07.561488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:38.851282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:41.132575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:43.648364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:46.078092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:48.409274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:50.857435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:53.188561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:55.619905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:57.901958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:00.360376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:02.848230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:05.145117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:07.735620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:39.026787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:41.440404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:43.832083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:46.261996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:48.624311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:51.046152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:53.372593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:55.803413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:58.080581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:00.544941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:03.044084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:05.311887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:07.909724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:39.184300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:41.627111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:44.017170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:46.429281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:48.809607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:51.207687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:53.556227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:55.970083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:58.391902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:00.716718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:03.213983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:05.494897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:08.092897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:39.368667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:41.800230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:44.196556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:46.611223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:48.976294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:51.390954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:53.738482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:56.153094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:58.583287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:00.899637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:03.397541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:05.678417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:08.266403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:39.547105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:41.981833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:44.379804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:46.778873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:49.159448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:51.558243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:53.921071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:56.321874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:47:58.752470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:01.082303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:03.581172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-23T14:48:05.844968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-09-23T14:48:15.358328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-23T14:48:15.600727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-23T14:48:15.845151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-23T14:48:16.227241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-23T14:48:08.520060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-23T14:48:08.774004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Country/RegionConfirmedDeathsRecoveredActiveNew casesNew deathsNew recoveredDeaths / 100 CasesRecovered / 100 CasesDeaths / 100 RecoveredConfirmed last week1 week change1 week % increaseWHO Region
0Afghanistan36263126925198979610610183.5069.495.04355267372.07Eastern Mediterranean
1Albania4880144274519911176632.9556.255.25417170917.00Europe
2Algeria27973116318837797361687494.1667.346.1723691428218.07Africa
3Andorra907528035210005.7388.536.48884232.60Europe
4Angola9504124266718104.3225.4716.9474920126.84Africa
5Antigua and Barbuda86365184053.4975.584.62761013.16Americas
6Argentina16741630597257591782489012020571.8343.354.211307743664228.02Americas
7Armenia3739071126665100147361871.9071.322.673498124096.89Europe
8Australia153031679311582536861371.0960.841.7912428287523.13Western Pacific
9Austria20558713182461599861373.4788.753.91197438154.13Europe

Last rows

Country/RegionConfirmedDeathsRecoveredActiveNew casesNew deathsNew recoveredDeaths / 100 CasesRecovered / 100 CasesDeaths / 100 RecoveredConfirmed last week1 week change1 week % increaseWHO Region
177United Kingdom3017084584414372544276887315.190.483190.2629694447641.60Europe
178Uruguay12023595121610132.9179.123.68106413812.97Americas
179Uzbekistan2120912111674941467855690.5755.041.0417149406023.67Europe
180Venezuela159881469959588352542130.9162.291.4712334365429.63Americas
181Vietnam43103656611000.0084.690.003844712.24Western Pacific
182West Bank and Gaza106217837526791152200.7335.332.088916170519.12Eastern Mediterranean
183Western Sahara1018100010.0080.0012.501000.00Africa
184Yemen16914838333751043628.5649.2657.981619724.45Eastern Mediterranean
185Zambia4552140281515977114653.0861.844.973326122636.86Africa
186Zimbabwe27043654221261922241.3320.046.64171399157.85Africa